Overexpression of the DNA Repair Gene, Ribonucleotide Reductase Small Subunit M2 (RRM2) Ying Z

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

Translational Cancer Mechanisms and Therapy Clinical Cancer Research A Novel Mechanism Driving Poor-Prognosis Prostate Cancer: Overexpression of the DNA Repair Gene, Ribonucleotide Reductase Small Subunit M2 (RRM2) Ying Z. Mazzu1, Joshua Armenia2,3, Goutam Chakraborty1, Yuki Yoshikawa1, Si'Ana A. Coggins4, Subhiksha Nandakumar2, Travis A. Gerke5, Mark M. Pomerantz6, Xintao Qiu6, Huiyong Zhao7, Mohammad Atiq1, Nabeela Khan1, Kazumasa Komura8, Gwo-Shu Mary Lee6, Samson W. Fine9, Connor Bell6, Edward O'Connor6, Henry W. Long6, Matthew L. Freedman6, Baek Kim4,10, and Philip W. Kantoff1

Abstract

Purpose: Defects in genes in the DNA repair pathways Results: Knockdown of RRM2 inhibited its oncogenic significantly contribute to prostate cancer progression. We function, whereas overexpression of RRM2 promoted hypothesize that overexpression of DNA repair genes may epithelial mesenchymal transition in prostate cancer cells. also drive poorer outcomes in prostate cancer. The ribonucle- The prognostic value of RRM2 RNA levels in prostate cancer otide reductase small subunit M2 (RRM2) is essential for DNA was confirmed in 11 clinical cohorts. Integrating the tran- synthesis and DNA repair by producing dNTPs. It is frequently scriptomic and phosphoproteomic changes induced by overexpressed in cancers, but very little is known about its RRM2 unraveled multiple oncogenic pathways downstream function in prostate cancer. of RRM2. Targeting RRM2 with COH29 showed excellent Experimental Design: The oncogenic activity of RRM2 in efficacy. Thirteen putative RRM2-targeting transcription fac- prostate cancer cells was assessed by inhibiting or overexpres- tors were bioinformatically identified, and FOXM1 was sing RRM2. The molecular mechanisms of RRM2 function were validated to transcriptionally activate RRM2 in prostate determined. The clinical significance of RRM2 overexpression cancer. was evaluated in 11 prostate cancer clinical cohorts. The efficacy Conclusions: We propose that increased expression of of an RRM2 inhibitor (COH29) was assessed in vitro and in vivo. RRM2 is a mechanism driving poor patient outcomes in Finally, the mechanism underlying the transcriptional activa- prostate cancer and that its inhibition may be of significant tion of RRM2 in prostate cancer tissue and cells was determined. therapeutic value.

Introduction putative drivers of cancer initiation and progression. Genomic instability results from DNA damage induced by genotoxic Prostate cancer is the third leading cause of cancer death in insults, dysfunction in pathways governing DNA repair, and American men. Advances in genomics allow the identification of abnormal cell-cycle control (1, 2). DNA-repair pathway defects are prevalent in advanced prostate cancer (3, 4). Defective DNA 1 Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New repair is correlated with an increased sensitivity to PARP inhibi- York. 2Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York. 3Oncology, IMED Biotech Unit, AstraZeneca, Cambridge, tors, platinum chemotherapy, or immunotherapy that can be fi United Kingdom. 4Center for Drug Discovery, Department of Pediatrics, Emory exploited for clinical bene t (5, 6). Ironically, overexpression of University School of Medicine, Atlanta, Georgia. 5Moffitt Cancer Center, Tampa, DNA repair genes may also contribute to cancer progression: Florida. 6Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, patients with prostate cancer with BRCA1-expressing tumors 7 Massachusetts. Antitumor Assessment Core Facility, Memorial Sloan Kettering more likely develop lethal cancer than those with BRCA1- 8 Cancer Center, New York, New York. Translational Research Program and nonexpressing tumors (7). Department of Urology, Osaka Medical College, Osaka, Japan. 9Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York. The nucleotide metabolism enzyme, ribonucleotide reductase 10Department of Pharmacy, Kyung-Hee University, Seoul, South Korea. (RNR), is essential for DNA synthesis and DNA repair by producing dNTP. Abnormal dNTP levels lead to infidelity of DNA Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/). replication, causing an increase in genomic instability (8). RNR activity is coordinated with cell-cycle progression to maintain the Corresponding Author: Philip W. Kantoff, Memorial Sloan Kettering Cancer fine balance between dNTP production and DNA replication (9). Center, 1275 York Avenue, New York, NY 10065. Phone: 212-639-5851; Fax: 929- 321-5023; E-mail: [email protected] During the cell cycle, levels of two RNR subunits, RRM1 and RRM2B, are constant but RRM2 levels fluctuate (10). As the Clin Cancer Res 2019;XX:XX-XX rate-limiting RNR enzyme, RRM2 levels control the cell-cycle- doi: 10.1158/1078-0432.CCR-18-4046 dependent activity of RNR. Overexpression of RRM2 is associated 2019 American Association for Cancer Research. with poorer outcomes in multiple cancers (11, 12). In mice,

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L-glutamine and antibiotic at 37 Cin5%CO.Cellswere Translational Relevance 2 authenticated by human short-tandem repeat profiling at the Despite the recognized oncogenic activity of ribonucleotide MSK Integrated Genomics Operation Core Facility. The prostate reductase small subunit M2 (RRM2) in multiple cancers, the cancer tissue microarray was purchased from US Biomax, Inc. lack of knowledge of its function in prostate cancer limits the (Catalog No. T195c). therapeutic application of its inhibitors. Here, we defined the mechanisms of the oncogenic function of RRM2 and unra- Gene silencing and gene expression veled its prognostic value in prostate cancer. Moreover, we SMARTpool siRNA were obtained from Dharmacon and trans- identified FOXM1 as the driver of RRM2 overexpression in fected with RNAiMAX (Invitrogen). Cells were harvested 48 or 72 prostate cancer. Importantly, we elucidated the effectiveness hours after transfection for protein and mRNA analysis. Lentiviral and molecular mechanisms of an RRM2 inhibitor, COH29. vectors encoding RRM2 were purchased from GeneCopoeia and Overall, our study not only reveals RRM2 as a prognostic transfected with psPAX2 packaging and pMD2.G envelope plas- marker for advanced prostate cancer, but also provides support mid to HEK293FT cells for 2 days using Lipofectamine 3000 for prostate cancer clinical trials targeting RRM2. (Invitrogen). Thereafter, prostate cancer cells were infected with viral supernatants in the presence of 8 mg/mL polybrene. Stable cells were generated using puromycin selection. A list of siRNAs and constructs is provided in Supplementary Table S4. Efficiency overexpression of RRM2 induces the development of lung cancer. of knockdown and overexpression was verified by qPCR and Defects in DNA mismatch repair synergistically promote RNR- Western blot analysis. induced carcinogenesis in lung neoplasms from RRM2 transgenic mice (13). RNA analysis, RNA-Seq, and immunoblotting Although the oncogenic role of RRM2 has been linked to Total RNA was extracted from cells and analyzed as previously promotion of epithelial–mesenchymal transition (EMT) and described (19). TaqMan gene expression assays (Life Technology) angiogenesis (14, 15), there has been no comprehensive molec- were used for relative gene expression (Supplementary Table S5) ular understanding of how RRM2 regulates key downstream by qRT-PCR. Transcript levels were normalized to levels of biological processes and signaling to control tumor initiation GAPDH transcript. RNA sequencing was performed by 50 million and progression. In particular, very little is known about the 2 50 bp reads in the MSK Integrated Genomics Operation Core. function of RRM2 in prostate cancer. Here, we uncovered the RNA-sequencing data were analyzed by Partek Inc. The data are oncogenic properties of RRM2 by integrating transcriptomic with available from GEO (GSE117921-GSE117924). Proteins were phosphoproteomic analysis, and we determined the significant extracted by RIPA buffer and protein concentration was deter- prognostic value of RRM2 in prostate cancer. Further, we eluci- mined by the Bradford method. Equal amounts of protein were dated the transcriptional regulation of RRM2 by FOXM1 in loaded and resolved by SDS-PAGE, then transferred to polyviny- prostate cancer cells. lidene difluoride membranes for immunoblotting. All antibodies used are listed in Supplementary Table S4. Materials and Methods Phosphokinase arrays (R&D Systems) were applied according Clinical cohort summary and characterization to the manufacturer's instructions. Briefly, protein was extracted Characteristics of patients with prostate cancer in the Physi- from cells treated with siRNAs or inhibitors. Changes in kinase cians' Health Study (PHS) and Health Professionals Follow-up activity were visualized by chemiluminescence on autoradiogra- Study (HPFS) cohorts are shown in Supplementary Table phy film and measured using ImageJ software. S1 (16, 17). mRNA expression profiling from high-density tumor areas and adjacent normal prostate tissue was reported (18). Cell viability, cell cycle, apoptosis, and DNA damage analysis Logistic regression was used to quantify the association of RRM2 Cells were treated with siRNAs or inhibitors. Cell viability expression (in quartiles) and lethal cancer (Supplementary was assessed using CellTiter-Glo luminescent cell viability assay Table S2). The analysis was adjusted for age at cancer diagnosis (Promega). At 72 hours posttransfection, cell cycle, and apoptosis (linear), calendar year of diagnosis, body mass index, current were detected using the Muse Cell Cycle Assay Kit and the Muse smoking at diagnosis (binary), and Gleason grade. An additional Annexin V and Dead Cell Kit (EMD Millipore). DNA damage was eleven publicly-available prostate cancer cohorts are summarized detected at 48 hours posttransfection of siRNAs or at 24 hours in Supplementary Table S3. treatment of inhibitors in cells by using Muse multicolor DNA Damage Kit (EMD Millipore). Cell culture and prostate cancer tissue microarray Normal human prostate cell lines (PWR-E1, PZ-HPV-7, Soft agar assays, wound healing, and invasion assays RWPE-1) and human prostate cancer cell lines (LNCaP, 22Rv1, Soft agar assays were performed in six-well tissue culture plates DU145, and PC-3) were purchased from ATCC. C4-2 cells were by placing cells (2–5 104/well) in 2 mL of 0.3% soft-agar above obtained from VitroMed. E006AA cells were provided by John a 2-mL layer of 0.5% agar. After 2 weeks' incubation, cells were T. Isaacs (The Johns Hopkins University School of Medicine, stained with 1 mg/mL MTT in medium for 1 hour. Colonies Baltimore, MD) and the LAPC-4 cell line was provided by were detected and counted using GelCount technology (Oxford Charles Sawyers [Memorial Sloan Kettering Cancer Center Optronix Inc.). (MSK), New York, NY]. Normal prostate cell lines were cultured Cells were seeded in six-well cell culture plates for 48 hours. A in Keratinocyte Serum-Free Medium (K-SFM; Kit Cat Number: scratch was made with a pipette tip in a confluent area. Triplicates 17005-042; Thermo Fisher Scientific). All other cell lines were were prepared in each condition. Photographs of each scratch maintained in 10% FBS supplemented with 2 mmol/L of were taken at 24 hours after scratching.

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Matrigel invasion assays were performed in Matrigel invasion the guidelines of the Institutional Animal Care and Use Com- chamber (Fisher Scientific) with cells on the top of chambers in mittee at MSK. serum-free media. 10% FBS in the lower chamber was used as chemo-attractant. After indicated times, cells in the bottom cham- Bioinformatic analysis of clinical cohorts ber were fixed in methanol and stained with crystal violet, photo- Data for various clinical cohorts were obtained from cBioPortal graphed, and counted under phase-contrast microscopy. for Cancer Genomics (22) and Oncomine (23). Heatmaps and volcano plots were generated using Rv3.4.3 (https://www. Chromatin immunoprecipitations-Seq, ChIP-qPCR, and R-project.org). Pathway analysis was performed using GSEA (24) luciferase assays and ToppGene (25). Radical prostatectomy tissue was prepared and chromatin immunoprecipitations (ChIP)-Seq performed as described pre- Statistical analysis viously (20) with 6-mg antibodies to androgen receptor (AR) and Results are reported as mean SE. Comparisons between t H3K27Ac antibody. DNA sequencing libraries were prepared groups were performed using an unpaired two-sided Student P < fi using the ThruPLEX-FD Prep Kit (Rubicon Genomics). Libraries test or Wilcoxon test ( 0.05 was considered signi cant). were sequenced using 50-base pair reads on the Illumina platform Disease-free survival (DFS) was examined for time since diagno- (Illumina) at Dana-Farber Cancer Institute. Related data analysis sis. Cox proportional hazard regression was performed adjusting for ChIP-Seq and peak calling followed the same protocol as for clinical and demographic factors. reported. The quantitation of signals on the 1-MB region of enhancer of RRM2 was performed from four normal prostate Results tissue samples and four tumor samples. The data are available from GEO (GSE118845). RRM2 functions as an oncogene in prostate cancer cells ChIP-qPCR was performed using the ChIP-IT High Sensitivity RRM2 is highly expressed in most prostate cancer cell Kit (Active Motif, #53040) with ChIP-grade H3K4me3, FOXM1 lines tested, regardless of AR status (Supplementary Fig. antibodies, and native IgG, according to the manufacturer's S1A). Four prostate cancer cell lines (LNCaP, C4-2, 22Rv1, instructions. DNA was analyzed via qPCR. All ChIP experiments and E006AA) have relatively high RRM2 protein levels, where- were completed with at least two biological replicates. as PC-3 cells have lower RRM2 protein expression (Supple- For luciferase reporter assays, siRNAs were transfected in mentary Fig. S1A). It has been reported that RRM2 is barely cells for 24 hours and 500 ng of RRM2 promoter reporters expressed in normal prostate tissue (26). However, in three (GeneCopoeia) containing 0 or 3 kb promoter sequence widely used normal prostate cell lines, RRM2 was expressed at of human RRM2 were cotransfected with Lipofectamine 2000 a high level similar to that in LNCaP cells (Supplementary Fig. (Invitrogen). At 24 hours posttransfection, cells were collected for S1B). We speculated that those immortalized prostate cell lines fl fi luciferase assays according to Promega's instructions. may not re ect the real gene pro ling of normal prostate cells. We further detected RRM2 protein expression by IHC staining in a prostate cancer tissue microarray which included nine dNTP pool assay cases of prostate cancer adenocarcinoma and one leiomyosar- This assay was performed as previously described (21). Cells coma, plus two tissue samples from normal prostates. Com- were lysed with 60% cold methanol and the dry pellet was pared with the two normal prostates that had very low levels of resuspended in water. Two microliters of sample were used in fi 0 32 RRM2, ve of the nine prostate cancer tissue samples showed the HIV-1 RT based primer extension assay. 5 P-endlabeled high expression of RRM2 (Supplementary Fig. S1C), indicating primer ("P"; 50-GTCCCTCTTCGGGCGCCA-30) was individually 0 the potential oncogenic role of RRM2 in prostate cancer. annealed to one of four different templates (3 -CAGGGA- fi 0 dNTP production (dATP, dCTP, and dTTP) was signi cantly GAAGCCCGCGGTN-5 ). The template:primer complex was inhibited by knockdown of RRM2 (siRRM2; Fig. 1A). In both extended by HIV-1 RT to generate one additional nucleotide LNCaP and C4-2 cells, siRRM2 induced DNA damage with sig- þ extension product ("P 1") for one of four dNTPs contained in nificant activation of DNA damage markers, including upstream the dNTP samples extracted from the cells. In this assay, the molar ATM and downstream H2A.X phosphorylation (Fig. 1B and C). þ amount of the P 1 product is equal to that of each dNTP Furthermore, siRRM2 led to cell growth inhibition, S-phase arrest, contained in the extracted samples, which allowed us to calculate and apoptosis (Fig. 1D–F). Similar phenotypes induced by siRRM2 and compare the amounts of the cellular dNTPs for the different were also detected in 22Rv1 cells, which express AR-V7, and 6 treatments. The dNTP amounts were normalized to 1 10 cells E006AA cells, which do not express AR (Supplementary Fig. for the comparisons. S1D–S1H). Therefore, the tumor suppressive effects of RRM2 inhibition are not related to AR status and are not cell line specific. Xenograft studies To further understand the oncogenic functions of RRM2, we NOG-SCID mice were implanted subcutaneously with C4-2 established RRM2-expressing stable LNCaP and PC-3 cell lines. cells. After palpable tumors developed (typically 100 mm3), six Overexpression of RRM2 significantly increased dNTP production mice per group received either vehicle or COH29 (200 mg/kg) by whereas knockdown of RRM2 decreased dNTP (Fig. 1G). Surpris- oral gavage twice a day for 3 weeks. Tumors were measured twice a ingly, increased RRM2 expression did not affect 2D cell growth week using calipers. Tissues were collected for IHC staining and (Fig. 1H), but promoted anchorage-independent 3D colony for- protein assays. Multiple proteins were assessed by IHC using mation in RRM2-overexpressing PC-3 cells (PC3-RRM2; Fig. 1I). antibodies of H2A.x (1:1000), MYC (1:100), RRM2 (1:2500), LNCaP-RRM2 cells showed both increasing 2D cell growth cleaved-caspase3 (1:300), prostate-specific antigen (PSA; 1:2,000; and 3D colony formation (Supplementary Fig. S2A and S2B). Supplementary Table S4). All animal care was in accordance with PC3-RRM2 cells had a six-fold higher wound healing rate and

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A siNS B DNA damage profile C siRRM2 DSB 1.2 siNS siRRM2 p-H2A.X 60 p-ATM 40 siNS siRRM2 siNS siRRM2 0.8 *** * RRM2 ** 20 *** ** LNCaP *** Neg. Pos. 0.4 0 60 rH2A.X *** 40 ATM Phospho. ATM Relative dNTP levels 0.0 20 C4-2 β-Actin

Hours 48 72 48 72 48 72 48 72 To tal DNA damage (% )

Neg. Pos. 0 dATP dCTP dGTP dTTP Neg. Pos. Neg. Pos. siNS siRRM2 LNCaP C4-2 H2A.X Phospho.

D siNS LNCaP E F 14 G0–G1 G2–M Late apoptosis siRRM2 LNCaP S Early apoptosis 120 LNCaP C4-2

12 siNS C4-2 siNS siRRM2 siNS siRRM2 100 C4-2 siRRM2 C4-2 *** 10 *** *** 80 80 PARP 8 60 LNCaP 6

*** 40 RRM2

*** 40 *** 4

Apoptotic (%) 20 β-Actin Cell population (%)

Relative cell viability 2 0 0 siNS -+ + - LNCaP C4-2 0 siNS +- + - Day 0 357 siRRM2 + -- + siRRM2 --+ +

I J G H EV RRM2 EV 80 *** 2.5 16 **** RRM2 EV

2.0 **** RRM2 0 hr 60 12 3 1.5 *** *** 40 8 2 1.0 20

1 24 hr 0.5 4 0 Wound closure rate (%)

Relative cell viability 0 0 EV RRM2

Relative dNTP levels 0.0 dATP dCTP dGTP dTTP Day 0 357 Colony formation ability EV RRM2 K L M N 1.2 *** *** RRM2-gfp *** *** 8 EV ** 10 RRM2 *** **** RRM2 0.8 6 8 CDH1 EV *** 0.4 4 6 **** CDH2 4 CDH3 2 ** Invasion ability 0.0 2 Snai1 Invasion ability

RRM2 0 0 β-Actin siNS EV RRM2 Relative mRNA levels 2 PC3-EV siRRM2siCDH1siCDH3siSnai1 VIM Slug EV RRM2 RRMCDH1CDH2CDH3 Snai1 Twist1ZEB1 PC3-RRM2

Figure 1. The oncogenic roles of RRM2 in prostate cancer. A, dNTP production in siRNA-transfected cells. dNTP was detected at both 48 and 72 hours after transfection of siRNAs in C4-2 cells. siNS, nonspecific siRNA. B, siRRM2-induced DNA damage. DNA damage marker activation was monitored in LNCaP and C4-2 cells using Muse multicolor DNA damage kit. C, Activation of H2A.X was confirmed by immunoblotting. D and E, Analysis of cell proliferation (D) and cell cycle (E)in transfected cells. F, Apoptosis detected by Annexin V assays and immunoblots. G, dNTP production in empty vector (EV)/RRM2-overexpressing PC-3 cells (PC3-EV; PC3-RRM2). H, Cell proliferation in stable cells. I, Soft agar assays of stable PC-3 cells. The colony numbers were normalized to those in the control cells. J, Wound healing assays after the scratch done for 24 hours. K, Invasion assays after cells were plated for 48 hours. L and M, EMT marker expression detected by qPCR (L) and immunoblots (M) in both EV- and RRM2-expressing PC-3 cells. N, Invasion assays after multiple siRNAs were transfected in PC3-RRM2 cells. Figure values represent the mean SE of three independent experiments. , P < 0.05; , P < 0.01; , P < 0.001; , P < 0.0001 vs. control groups treated with empty vector (EV) or with nonspecific (siNS) siRNA.

level of invasiveness than control PC-3 cells (Fig. 1J and K), (SNAI1 and SLUG) were upregulated in PC3-RRM2 cells (Fig. 1L). indicating that RRM2 induced EMT. Mesenchymal markers Intriguingly, P-cadherin (CDH3), a cell-to-cell adhesion mole- including N-cadherin (CDH2) and vimentin (VIM) were not cule, was upregulated nine-fold in PC3-RRM2 cells, whereas affected by increased RRM2 expression, whereas the EMT markers E-cadherin (CDH1) expression was increased about ﬁve-fold

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(Fig. 1L and M). CDH3 has been observed in the development and RRM2 overexpression is prognostically significant in patients progression of multiple cancers including prostate cancer (27). with prostate cancer CDH3 and CDH1 coregulate collective migration in breast can- Overexpression of RRM2 leads to increased genomic instabil- cer (28). Therefore, RRM2 may promote collective migration by ity (29), contributing to cancer progression and resistance upregulating key regulators such as CDH1, CDH3, and SNAI1. to anticancer treatments (30). The copy number variation mea- This hypothesis was supported by showing that knockdown of sured by fraction of the genome altered (FGA) has been shown to RRM2 and each of these EMT regulators significantly attenuated be associated with Gleason score and the development of metas- RRM2-induced invasiveness in PC3-RRM2 cells (Fig. 1N; Supple- tases in patients with prostate cancer (3, 31). Here, for the first mentary Fig. S2F). In LNCaP-RRM2 cells, 2.4-fold increased time, the positive correlation between RRM2 level and overall invasiveness was detected, coupled with a five-fold upregulation FGA was observed in both the Cancer Genome Atlas (TCGA) of CDH2 and CDH3 (Supplementary Fig. S2C–S2E), indicating cohort (r ¼ 0.5; P ¼ 3.14E15; Fig. 2A and B; ref. 3) and Taylor RRM2 may activate specific mediators of EMT or collective migra- cohort (r ¼ 0.5, P ¼ 1.7E07; Supplementary Fig. S3C; ref. 32). In tion markers during different disease states. Taken together, this the two advanced prostate cancer cohorts (SU2C/PCF and Kumar provides strong evidence that RRM2 has oncogenic properties in cohorts; refs. 4, 33), weaker but significant correlations were seen vitro. (Supplementary Fig. S3E and S3F). Correspondingly, a significant

n A TCGA, Cell 2015 ( = 333) B r P − C = 0.5 = 3.14E 15 P FGA < 0.0001 11 CGS 0.5 10 0.4 RRM2 12% 9 BRCA2 18% 0.3 8 CHEK2 14% 0.2 7 ATM 13% 6 RAD51D 10% FGA signal 0.1 5

Genetic alteration Missense mutation Truncating mutation Germline mutation RRM2 level (log) 0.0 4 Deep deletion Shallow deletion mRNA upregulation No alterations 64 75 8 9 10 11 6 7 ≥8 Fraction genome altered (FGA) 01Clinical Gleason sum (CGS) 6 10 RRM2 level (log) Gleason score

Taylor (2010) Grasso (2012) D PHS/HPFS PHS/HPFS E F Tumor tissue Matched normal tissue **** **** 2 **** 6 P 4 P = 0.85 **** 4 trend < 0.01 trend **** 4 1 2 2 2 0 0 0 -1 -2 RRM2 level (log)

0 RRM2 level (log) RRM2 mRNA level -2 -2 -4 PG Pri Met 5-6 3+4 4+3 89-10 5-6 3+4 4+3 89-10 PG Pri Met n = 30 n = 131 n = 19 n = 23 n = 56 n = 31 Gleason grade Gleason grade

G H PHS/HPFS n Tumor tissue TCGA provisional ( = 499) Taylor (2010) Glinsky (2004) Low RRM2 P 6 trend = 0.003 100 100 100 High RRM2 5 4 80 80 80 P < 0.0001 3 60 P < 0.0001 60 60 P < 0.0001 2 1 40 40 40 1 (ref.) 0.68 0.77 3.12

DFS (% ) (0.29−1.57) (0.32−1.82) (1.4−6.96) 20 20 20 OR for RRM2 lethal 0 0 0 disease CI (95%) level 0150 00 150 0 50 100 150 0 50 100 150 Time (months) 21 3 4 Quartiles of RRM2 expression

Figure 2. Overexpression of RRM2 positively associates with poorer clinical outcomes in multiple prostate cancer clinical cohorts. A–C, The correlation of gene alteration with the fraction of genome altered (FGA) and Gleason grades in TCGA cohort is visualized in A. The statistical quantitation is shown in B and C. D, The correlation of RRM2 level with Gleason grade in tumor and matched normal tissues in the PHS/HPFS cohorts. E and F, The correlation of RRM2 expression with tumor progression in Taylor and Grasso cohorts. RRM2 levels were analyzed in prostate gland (PG), primary (Pri), and metastasis (Met) tissue samples. G, The association of RRM2 expression and the DFS in the TCGA, Taylor, and Glinsky cohorts. H, The correlation of RRM2 with the risk of lethal prostate cancer over long-term follow-up, independent from clinical characteristics and Gleason grade, in the combined HPFS and PHS prostate cancer cohorts. The odds ratios for lethal disease were adjusted for Gleason score. , P < 0.0001 vs. comparator groups.

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positive correlation between RRM2 level and Gleason score was showed that the Down-gene set was activated in metastatic observed in TCGA (Fig. 2C) and Taylor cohort (Supplementary patients with high RRM2 expression (Fig. 3C, left). These genes Fig. S3D). Consistent with other studies (3), alteration of DNA were significantly correlated with DFS (Fig. 3C, right). The find- repair genes (BRCA2, CHEK2, ATM, and RAD51D) in the TCGA ings were validated in five other prostate cancer clinical cohorts, cohort was significantly correlated with high FGA (Fig. 2A; Sup- supporting the clinical relevance of the genes regulated by inhi- plementary Fig. S3A). Higher RRM2 levels were significantly bition of RRM2 (Supplementary Fig. S4C). associated with heterozygous loss of CHEK2 and ATM (Supple- To fully understand the oncogenic role of RRM2 in prostate mentary Fig. S3B), suggesting their contribution to the higher FGA cancer, we evaluated the transcriptomic changes in PC-3-RRM2 and Gleason score in prostate cancer. cells (Supplementary Fig. S4D). GSEA analysis showed significant Besides publicly available clinical cohorts, we further vali- enrichment in EMT and angiogenesis (AGO) gene sets (Fig. 3D; dated the prognostic significance of overexpression of RRM2 refs. 36, 37), which strongly supported the phenotypes induced by within the HPFS (n ¼ 254) and the PHS (n ¼ 150), two cohorts RRM2 overexpression (Fig. 1I–N). The upregulated gene sets with long-term follow-up for fatal outcomes. Significantly induced by RRM2 overexpression may contribute to its oncogenic higher levels of RRM2 were observed with increasing Gleason properties in prostate cancer. Following integration of RRM2- score (Ptrend < 0.01; Fig. 2D, left) in tumor, but not in matched induced genes in cell lines and genes, which are positively cor- normal tissue (Ptrend ¼ 0.85; Fig. 2D, right). In both Taylor and related with RRM2 upregulation in three clinical cohorts, we Grasso cohorts (32, 34), the levels of RRM2 were significantly identified the key 126 RRM2-regulated genes relevant to clinical higher in metastatic tissue than in primary and normal tissue outcome (Fig. 3E). These 126 genes were upregulated in meta- (Fig.2EandF).IntheTCGA,Taylor,andGlinskycohorts(3,32, static patients in two additional clinical cohorts (Supplementary 35), RRM2 levels in the highest quartile were significantly Fig. S4E). associated with an increased rate of biochemical recurrence Integration of two data sets from siRRM2 RNA-Seq in C4-2 cells (Fig. 2G). and RRM2-expressing RNA-Seq in PC-3 cells provided an in- Importantly, in HPFS/PHS cohorts, high RRM2 expression was depth understanding of the RRM2-regulated molecular mechan- significantly associated with increased risk of lethal disease (Sup- isms. Fifty-eight common genes were significantly enriched in plementary Table S2). When combining the HPFS and PHS oncogenic pathways (FOXM1, PLK1, MYC, and mTORC1 signal- cohorts, RRM2 RNA levels in the highest quartile, compared with ing) and in glycolysis and cell-cycle–related hallmark gene sets the lowest quartile, were associated with a 4.0 times higher risk of (Supplementary Fig. S4F). lethal disease (95% CI, 2.1–7.7; Supplementary Table S2). When adjusting for patient and tumor characteristics including centrally RRM2 inhibitor COH29 induced molecular mechanisms of reviewed Gleason grade, this association remained strong (odds tumor suppression ratio, 3.1; 95% CI, 1.4–7.0; Supplementary Table S2; Fig. 2H). Given the potential oncogenic role of RRM2, multiple RNR There was no association with lethal prostate cancer for RRM2 in inhibitors have been developed for clinical application. The matched normal tissues (Supplementary Fig. S3G). Altogether, recent novel RNR inhibitor COH29wasdesignedtotargeta these results suggest that increased expression of RRM2 is asso- ligand pocket of RRM2 and showed low side effects (38, 39). ciated with adverse biology and poor outcomes. COH29 is currently in a phase I trial to treat multiple solid tumors. Here, we assessed the effects of COH29 on prostate Transcriptomic changes induced by regulation of RRM2 cancer cells. COH29 inhibited dNTP production due to inhi- unraveled the clinically relevant RRM2 signature in prostate bition of RRM2 activity in multiple cancer cell lines. Similarly, cancer dose-dependent inhibition of dNTP production by COH29 was Although the oncogenic role of RRM2 has been reported in detected in C4-2 cells (Fig. 4A). Similar to siRRM2, COH29 led several cancer types, an understanding of the comprehensive to DNA damage (Fig. 4B and C); strong cell growth inhibition molecular mechanisms by which it functions is lacking. We in multiple prostate cancer cell lines (Fig. 4D); S-phase arrest detected global transcriptomic changes induced by siRRM2 (Fig. 4E; Supplementary Fig. S5A); and apoptosis (Fig. 4F; (Supplementary Fig. S4A). The gene ontology (GO) analysis Supplementary Fig. S5B). revealed that inhibition of RRM2 repressed genes enriched in The COH29-induced global transcriptomic changes are dose some key biological processes (e.g., cell proliferation, cell dependent (Fig. 4G). More than 98% of the altered gene expres- death, protein metabolic process, and cell cycle) and oncogenic sion in high (20 mmol/L) and low (10 mmol/L) doses of COH29 pathways (e.g., MYC pathway, PLK pathway, WNT signaling, overlapped (Supplementary Fig. S5C and S5D); therefore, only Aurora B signaling, and FOXM1 pathway; Supplementary Fig. the RNA-Seq dataset of high-dose COH29 was applied for the S4B). The gene set enrichment analysis (GSEA) further defined sequential GO analysis. GSEA analysis of the mRNA changes the highly enriched pathways (MYC, E2F, cell cycle, p53 sig- induced by COH29 exhibited similar gene set enrichment as was naling, apoptosis; Fig. 3A). Targets of MYC, cell cycle, and E2F seen with siRRM2 treatment, including MYC targets, E2F targets, pathways were significantly inhibited in siRRM2-treated cells the p53 pathway, and the apoptosis pathway (Fig. 4H). Addi- (Fig. 3B). Five common genes in the p53 and apoptosis gene set tionally, COH29 inhibited the androgen response gene set pathways were validated to be upregulated by siRRM2 treat- (Fig. 4H). Compared with the siRNA approach, COH29 induced ment (Fig. 3B). wider transcriptomic changes (Supplementary Fig. S5E). We To evaluate the clinical relevance of inhibition of RRM2, we first applied GO analysis by using genes whose expression were looked at siRRM2-downregulated genes that were positively cor- similarly altered (219 downregulated genes; 106 upregulated related with RRM2 in the Taylor cohort (named as "Down-gene"), genes) with COH29 and siRRM2 treatment. The top five inhibited and upregulated genes which negatively correlated with RRM2 pathways were PLK, MYC activation, FOXM1, Aurora B, and expression level (named as "Up-gene"; Fig. 3C). The heat maps telomerase pathways, whereas p53 downstream pathway and

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A Up-gene Down-gene C

MYC targets E2F targets Cell cycle p53 pathway Apoptosis Taylor (2010) class_down=High Type class_down=low RRM2 1.00 0.75 0.50 P NES:-2.78 NES:-1.41 NES:-1.99 NES:3.05 NES:1.74 0.25 = 0.0068 FDR:0.0 FDR:0.15 FDR:0.0 FDR:0.0 FDR:0.07 0.00 Down-gene 500 100 150 class_up=High B MYC targets E2F targets/cell cycle p53/Apoptosis pathway class_up=low + +++ 1.00 +++++ +++ ++++++++++++++++++++++++++++++ ++ + + +++++++

1.5 Probability of DFS 1.5 0.75 +++ +++++++++++ siNS +++++++++++++++++ + 5 ++++++++ siRRM2 0.50 +++++ 1.0 1.0 4 Up-gene P 3 0.25 = 0.00034 0.5 0.5 2 0.00 500 100 150 1 Primary Metastasis -2 0 2 0.0 Z Time (months) Relative mRNA levels 0 Row -score A A2 NCL PLK1 BAX FAS MT PTMA CDC20CENPE TOP2A BTG2 FDXR GADD45A

D Up-gene Down-gene E Taylor (2010) Taylor (2010) + Positively correlated Type High + Low ++ ++ with RRM2 in 3 cohorts RRM2 +++ +++++++++++++ 1.00 + PC-3 +++ RRM2-Upregulated PC-3 RRM2-Upregulated ++ +++ (626) 0.75 +++++++ +++ + ++ PC-3 + PC-3 +++ + + + + (1230) (1230) 126 0.50 41 39 0.25 HR = 3.1, P = 0.0049

(200) (470) (1230) DFS probability 95% CI, 1.03–9.1 EMT AGO 0.00 NES:2.18 Upregulated NES:1.4 Primary Metastasis 500 100 FDR:0.02 FDR:0.2 in PC3-RRM2 Time (months)

Figure 3. Transcriptomic analysis unraveled the molecular mechanism of RRM2 function. A, GSEA of transcriptomic changes. The histograms show the distribution of select top GSEA molecular signatures: MYC targets (MYC_up_V1_up gene set), E2F targets (hallmark_E2F_targets gene set), cell cycle (Module_54 gene set), p53 pathway (hallmark_p53_pathway), and apoptosis (hallmark apoptosis). Up-gene (upregulated genes); Down-gene (downregulated genes). B, Multiple targets of pathways were validated by qRT-PCR. C, siRRM2-regulated gene profiling and the correlation of these genes with DFS in Taylor cohort. D, Significant enrichment in EMT and Angiogenesis gene sets in RRM2-overexpressing PC-3 cells. E, RRM2-regulated 126-gene profiling and correlation with the clinical outcome in clinical cohorts. One hundred and twenty-six genes were revealed by overlapping 1,230 upregulated genes in PC3-RRM2 cells with 627 common genes positively correlated with RRM2 overexpression in three prostate cancer cohorts (TCGA, Kumar, and SU2C/PCF). The correlation of these genes with DFS was analyzed in the Taylor cohort.

MYC repressive pathway were activated with both approaches to sive tumor in both cohorts (Supplementary Fig. S5H and S5I). inhibit RRM2 activity (Supplementary Fig. S5F). The major inhib- These results suggested that this gene panel can be targeted by ited biological processes included cell cycle, RNA processing, inhibition of RRM2 in multiple cancer types in which RRM2 response to DNA damage, and chromosome organization–relat- drives aggressiveness. ed processes (Supplementary Fig. S5G). Intriguingly, besides apoptosis, DNA damage, and cell proliferation, the processes Phosphoproteomic changes are induced by modulating RRM2 for signal transduction, regulation of kinase activity, and inhibi- expression in prostate cancer cells tion of phosphorylation were activated by inhibition of RRM2 The GO analysis of genes upon inhibition of RRM2 revealed (Supplementary Fig. S5G). that signal transduction and kinase activity processes were There are 33 genes that were inhibited by siRRM2/COH29 and affected (Supplementary Fig. S5G). Therefore, we applied the positively correlated with RRM2 upregulation in the Taylor phosphokinase array in siRRM2- and COH29-treated C4-2 cohort; 12 genes were upregulated with inhibition of RRM2, but cells (Fig. 5A; Supplementary Fig. S6A). The array detected negatively correlated with RRM2 upregulation (Fig. 4I). This the phosphorylation of 43 human kinases, which are involved RRM2-regulated gene profile was validated in three clinical in several key oncogenic signaling processes. Both treatments cohorts (Fig. 4J). The 33 downregulated genes were activated in significantly inhibited AKT/mTOR signaling, leading to the metastatic samples, whereas the 12 upregulated genes were down- downstream effectors inhibition. Besides AKT/mTOR signal- regulated in metastatic samples (Fig. 4J). RRM2 showed onco- ing, both treatments inhibited multiple components in STAT genic activity in colorectal and breast cancer progression (13, 14). signaling and SFK signaling (Fig. 5A and B; Supplementary Fig. Therefore, we applied the 33 downregulated genes in the Gaedcke S6A and S6B). The inhibition of these signaling pathways colorectal cohort and Curtis breast cancer cohort (40, 41). The induced by COH29 was dose dependent (Fig. 5A and B). These expression of the 33 genes are significantly upregulated in aggres- results were confirmed by Western blotting (Fig. 5C). The

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B A DNA damage profile C 0 μmol/L p-ATM/H2A.x μ μ μ 1.4 μ COH29 0 mol/L 10 mol/L 20 mol/L 40 p-H2A.X COH29 (μmol/L) 10 mol/L p-ATM 1.2 μ 20 mol/L Pos. 00201 001 20 1.0 20 * * rH2A.X 0.8 * * 0 LnCap 0.6 * ** 60 * ** p-Chk1 0.4 * 40

* Phospho. ATM

20 ** C4-2 β-Actin

0.2 Neg.

Neg. damage (%) DNA Total 0 Relative dNTP levels Relative dNTP 0 Pos. dATP dGTP dTTP dCTP H2A.X Phospho. 0 10 20 LNCaP C4-2 COH29 (μmol/L) D E F − − COH29 (μmol/L) 150 LNCaP G0 G1 S G2 M Late apoptosis 22Rv1 120 100 Early apoptosis 002 0 20 C4-2 LNCaP C4-2 100 C4-2 100 E006AA 80 PARP 80 LNCaP *** *** *** 60 *** Cleaved 60 caspase-3 40 50 40

Cell viability (%) β-Actin 20 20 Apoptotic cells (%) Cell population (%) 0 0 0 LNCaP C4-2 2.5 2040 0 603010 COH29 -+ +- COH29 -+ +- G COH29 (μmol/L) H MYC targets E2F targets p53 pathway Apoptosis Androgen response

#1 #2 #1 #2 #1 #2 NES:-1.97 NES:-1.41 NES:2.19 NES:-2.59 NES:-2.86 0 10 20 FDR:0.007 FDR:0.0 FDR:0.014 FDR:0.0 FDR:0.0 COH29 (μmol/L)

I J Grasso (2012) Lapointe (2004) Yu (2004) Positively correlated Type with RRM2 Down-genes RRM2

373 819761 33 down-genes

Taylor cohort Taylor 152 9682 siRRM2/COH29 siRRM2/COH29

Negatively correlated Up-genes with RRM2 12 up-genes Normal Primary Metastasis −2 0 2 Row Z−Score

Figure 4. The tumor suppressive role of COH29 (RRM2 inhibitor). A, dNTP production in COH29-treated C4-2 cells. Two doses of COH29 were used in C4-2 cells, and dNTP production was detected after 24 hours of treatment. B and C, Activation of DNA damage markers. D, Cell proliferation assay. E, Cell-cycle analysis after 48 hours of COH29 treatment. F, COH29-induced apoptosis. G, The global mRNA changes induced by COH29 in C4-2 cells, after 48 hours of treatment. H, GSEA analysis of mRNA proﬁling in 20 mmol/L of COH29-treated cells. The histograms showed the distribution of select top GSEA molecular signatures. Up-gene (COH29- induced upregulated genes); Down-gene (COH29-induced downregulated genes); NES, normalized enrichment score; FDR, false discovery rate. I and J, Identiﬁcation of targeted genes by inhibition of RRM2. Genes affected by inhibition of RRM2 in cells were overlapped with genes correlated with RRM2 overexpression in Taylor cohort to reveal 33 downregulated genes and 12 upregulated genes (I). These gene panels were validated in three additional prostate cancer cohorts (J). Figure values represent the mean SE of three independent experiments. , P < 0.05; , P < 0.01; , P < 0.001 vs. control groups.

activation of DNA damage markers was detected when the RRM2 inhibitor COH29 has antitumor effect in vivo activity of these kinases was inhibited. Together, multiple Because the 126 genes induced by RRM2 are highly corre- oncogenic signaling pathways were repressed by inhibiting lated with clinical outcome, targeting them may provide ther- RRM2. apeutic beneﬁt (Fig. 3E). We applied ToppGene analysis by

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A B 0 μmol/L 10 μmol/L 20 μmol/L C COH29 (μmol/L) GBFEDCAGB FEDCAGB FEDCA AKT/mTOR signaling STAT signaling SFK signaling 1.2 1 1.2 1.2 01020 siNS 2 siRRM2 3 0.8 0.8 AKT_T308 4 0.8 5 PRAS40_T246 6 0.4 0.4 0.4 7 Src_Y419 8 Relative density 9 0.0 0.0 0.0 10 Stat5a/b 11 Y694/Y699 12 Chk-2_T68 p-Yes 13 p-Src p-Hck p-FAK 14 AKT_S473 AKT_T308 p-TOR p-p70S6K* p-p70S6K p-eNOS p-PRAS40 rH2A.X p-STAT2 p-STAT5a/b p-STAT5b 15 pSTAT6 16 17 RRM2 18 D1-2 F5-6 E3-4 F1-2 F9-10 E9-10 D7-8 E7-8 β B9-10 B11-12 C1-2 D15-16 G3-4 C11-12 0 μmol/L 10 μmol/L 20 μmol/L D11-12 -Actin Location COH29

D COH29 COH29 E F Lucanthone 104 104 Vehicle COH29 900 Vehicle P

62 3 1.0 = 0.0012 COH29 0.8 126-gene 126-gene 600 signature 0.6

signature *** 67 43 0.4 300 77 0.2 61 Docetaxel Docetaxel+ADT

Palbociclib Dasatinib weight (g) Tumor 0 (in PC-3 cells) (in patients) size (mm ) Tumor 0 04 711141821 Vehicle COH29 Toppgene analysis First-line treatment Treatment (days)

G H Vehicle COH29 Vehicle COH29 Vehicle COH29 γH2A.X

MYC MYC γH2A.X AR

PSA

PSA 50 µm Cleaved 50 µm β

Caspase3 -Actin

Figure 5. Phosphoproteomic changes induced by inhibition of RRM2 and COH29 efficacy in vivo. A and B, Phosphokinase array analysis after 24-hour COH29 treatment in C4-2 cells. The whole-cell lysates were collected for human phosphokinase array analysis. Each membrane contains kinase-specific antibodies (number indicated). Relative phosphorylation of spots was quantified by Image J software, and the value of vehicle (0 mmol/L) was set up as "1" (B). C, Validation of phosphokinase array by immunoblots. For siRNA-treated groups, cell lysates were collected after transfection for 48 hours. The representative blots for each condition are shown, and the values represent the mean SE of two independent experiments. D, Druggable RRM2 signatures. Top three drugs from ToppGene analysis and COH29 are shown (left). Numbers of genes downregulated by docetaxel (in PC-3 cells) and docetaxel þ ADT (in patients) are shown (right). E and F, Antitumor effects of COH29 in vivo. Tumor volumes and weights were measured following oral administration of COH29 (200 mg/kg) in established C4-2 xenograft tumors (n ¼ 6 per group). Values are means SE. , P < 0.001 versus vehicle mice. G and H, Regulation of key genes by COH29 in vivo. Multiple genes regulated by COH29 were assessed in xenograft tumors by IHC staining (G) or immunoblotting (H). using the 126-gene signature (25). The top three drugs that (Fig.5EandF),withoutsignificant difference in body weight of target the signature are lucanthone, palbociclib, and dasatinib. the mice (Supplementary Fig. S6C). The COH29-treated xeno- The numbers of genes for each drug potentially targeting the grafts showed increased H2A.X phosphorylation and cleaved signature are shown (Fig. 5D). We also evaluated whether caspase-3, and decreased MYC and PSA (Fig. 5G and H). We docetaxel as the first-line treatment of prostate cancer affects further detected significant inhibition of AR mRNA and protein these genes by using two profiling datasets of docetaxel treat- expression in COH29-treated tumors (Fig. 5H; Supplementary ment in PC-3 cells (GSE83654) and docetaxel plus androgen Fig. S6D). These results are consistent with the repression of deprivation therapy (ADT) in patients (42). Of 126 genes, 67 androgen response genes by COH29 in vitro (Fig.4H).Inter- (from docetaxel-treated PC-3 cells) and 43 genes (from doc- estingly, we did not observe inhibition of AR by siRRM2 etaxel þ ADT-treated patients) were downregulated (Fig. 5D, treatment (data not shown), indicating COH29-inhibited AR right). Compared with the other drugs mentioned, COH29 may not be directly associated with inhibition of RRM2. inhibited the highest number of genes in the signature (104 of Together, COH29 has antitumor effects in vivo in a human 126 genes; Fig. 5D), due to its RRM2-specific inhibition that prostate cancer xenograft model. was previously reported (39). We further assessed the antitumor activity of COH29 in vivo. RRM2 is transcriptionally activated by FOXM1 in prostate After 3 weeks of treatment, COH29-treated tumor bearing mice cancer had significantly lower tumor volume (231 mm3) and tumor Several studies have focused on transcriptional activation and weight (0.27 g) than the vehicle group (744 mm3 and 0.57 g) delayed degradation of RRM2 in cancers (29, 43–45).

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Amplification and mutation of RRM2 is rare in prostate cancer. databases to seek potential TFs targeting RRM2. We searched Transcriptional regulation may largely contribute to the over- genes that positively correlated with RRM2 expression (r > 0.5, expression of RRM2 in prostate cancer. We applied H3K27Ac P < 0.05) in four widely used clinical cohorts (Fig. 6B). Among ChIP-Seq in prostate normal and tumor tissues. Significantly all the candidates, 13 genes are TFs. Interestingly, E2F1 and higher H3K27Ac binding signal was detected at the enhancer of E2F3 have been demonstrated to target RRM2 in colorectal RRM2 (1 Mb region) in prostate cancer (n ¼ 4, P < 0.05), cancer and KB cells (43, 44). compared with normal prostate tissue (n ¼ 4; Fig. 6A), indicating A correlation matrix from the TCGA (primary tumor samples) more activated RRM2 transcription in prostate cancer than in and SU2C/PCF (metastatic samples) cohorts not only showed the normal tissue. relationships between RRM2 and all potential RRM2-targeting TFs A few transcription factors (TF) have been identified that but also showed the correlations among the TFs (Supplementary target RRM2 in different cancers (e.g., E2F1, E2F3, and MYCN; Fig. S7A). It revealed different correlation patterns for the TCGA refs. 43–45), but there are no studies of transcriptional regu- and SU2C/PCF cohorts, indicating that disease state may con- lation of RRM2 in prostate cancer cells. Here, we developed a tribute to the specificity of RRM2-targeting TFs in prostate cancer. strategy that combines prostate cancer clinical cohorts and TF The subset of RRM2-targeting TFs which correlate with significant

A H3K27Ac-ChIP Seq in tissue B Search genes correlated with RRM2 expression C (r > 0.5, P < 0.05)

PHS/HPFS TCGA SU2C/PCF Kumar MSKCC(Taylor) r = 0.4 (95% CI, 0.31−0.47) n = 236 n = 253 n = 208 n = 212 4.0 1558 human transcription factors Indolent Lethal

Tumor normal Tumor E2F1 E2F1 E2F1 E2F3 3.0 RRM2 E2F2 E2F7 E2F2 E2F7 E2F3 E2F8 E2F7 E2F8 RRM2 level 2.0 0.8 * E2F8 FOXM 1 E2F8 FOXM 1 0.6 FOXM 1 MYBL2 FOXM 1 MYBL2 0.4 HM GB2 POU5F1 HM GB2 WHSC1 MYBL2 SSRP1 ZNF165 2.0 3.0 4.0 5.0 0.2 FOXM1 level WHSC1 ZNF367 ZNF367 Normal tumor ZNF367 (4) (4) 2 cohorts 3cohorts 4 cohorts D E Chr2: 10020210 10121200 00722101 ChIP with P1 primers ChIP with P2 primers ChIP with P3 primers 1.2 20 1.2 0.2 1 MDA-MB-231 C4-2 ** ** 22Rv1 2 MCF7 15 0.8 0.8 3 HeLa 10 0.1 % Input MCF-7 0.4 ** ** 0.4 4 5 ** ** RRM2 0 0 0 0 P1 P2 P3 FOXM1 IgG H3K4me3 IgG FOXM1 IgG FOXM1 IgG

F siNS G H I 5 siFOXM1 siNS 1.2 siFOXM1 22Rv1 C4-2 1.2 NT FDI-6 4 RRM2 3 *** 0.8 0.8 ** ** FOXM1 ** 2 ** 0.4 0.4 ** *** *** 1 β-Actin

Luciferase activity 0 0 0 Relative mRNA levels Relative mRNA R2-0k R2-3K levels Relative mRNA 22Rv1 C4-2 siNS siNS RRM2 MYC PSA siFOXM1 siFOXM1 FOXM1 CENPF

Figure 6. Transcriptional activation of RRM2. A, H3K27Ac ChIP-Seq in tissues. The binding signal on RRM2 enhancer (1Mb region) was quantiﬁed. B, The strategy to identify RRM2-targeting TFs. Human TFs were selected in the genes positively correlated with RRM2 expression in prostate cancer cohorts. Yellow/orange/pink bars indicate that TFs appear in four/three/two cohorts. C, The correlation of FOXM1 and RRM2 in PHS/HPFS cohorts. D, FOXM1 binding on RRM2 promoter in cancer cells. The overview of multiple ChIP-Seq datasets were extracted from Cistrome Data browser. P1–P3 primers were designed for FOXM1 ChIP-PCR. E, FOXM1 or H3K4me3-ChIP-PCR on RRM2 promoter. F, RRM2 promoter activity regulated by FOXM1. The reporters without (R2-0K) or with (R2-3K) 3kb RRM2 promoter sequence were transfected in siRNA-treated 22Rv1 cells. G and H, Inhibition of RRM2 expression by siFOXM1 in 22Rv1 and C4-2 cells. I, Inhibition of FOXM1 targets by FDI-6 (20 mmol/L) in 22Rv1 cells. Figure values represent the mean SE of three independent experiments. , P < 0.05; , P < 0.01; , P < 0.001 vs. control groups.

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clinical outcome should be more meaningful and could facilitate (Supplementary Fig. S3B). Overexpression of RRM2 could be the development of future therapeutic strategies. Among 13 TFs, required to compensate for the DNA repair deficiency induced both FOXM1 and E2F8 were shared in all the cohorts (Fig. 6B). by deletion of DNA repair genes (e.g., CHEK2 or ATM). However, FOXM1 has been reported to be one of the major drivers in the correlation of RRM2 with FGA and clinical Gleason sum prostate cancer (46). Besides those four cohorts (Fig. 6B), the (CGS) is not completely dependent on the deletion of both genes, positive correlation between FOXM1 and RRM2 was also because some tumors showed high RRM2 expression without observed in PHS/HPFS cohorts (Fig. 6C). Similar to RRM2, deletion of CHEK2 and ATM (Fig. 2A). FOXM1 level is highly correlated with DFS in the Taylor cohort Similar to what was reported in breast cancer (14), overexpres- (Supplementary Fig. S7B). For both FOXM1 and RRM2, higher sion of RRM2 in prostate cancer cells promoted 3D colony levels are associated with lethal disease (P < 0.0001; Supplemen- formation and invasion. Unexpectedly, RRM2 activated tary Fig. S7C). atypical EMT progression by upregulating E-cadherin (CDH1) To assess the transcription control of RRM2 by FOXM1, we first and P-cadherin (CDH3). High CDH1 expressed PC-3 cells identified FOXM1 binding region to the RRM2 promoter from showed more aggressive phenotypes (47). Similarly, moderate published FOXM1 ChIP-Seq. In breast cancer cells and HeLa cells, CDH1 levels present in 4T1 breast cancer cells could promote there were two major FOXM1 binding peaks in the promoter of cancer metastasis by increasing the collective migration (48). The RRM2 (Fig. 6D). Although no FOXM1 ChIP-Seq data in prostate essential role of CDH3 in collective migration could promote cancer cells is available, ChIP-Seq of transcriptional activation tumorigenesis in prostate, colon, ovary, and breast cancers (49). marks (including H3K4me3, H3K27Ac, and POLR2A) in multiple Coexpression of CDH1 and CDH3 leads to the aggressive biology prostate cancer cell lines provided supporting evidence that the behavior in breast cancer cells due to interruption of the interac- FOXM1-binding regions of RRM2 promoter are activated (Sup- tion between CDH1 and catenins (48). Therefore, upregulation of plementary Fig. S7D). Two pairs of primers (P1 and P2) were CDH1 and CDH3 by RRM2 overexpression could be one of the designed to evaluate FOXM1 binding in prostate cancer cells, driving forces of the more aggressive phenotype in PC-3-RRM2 based on the ChIP-Seq data from other cells. A negative control cells. Furthermore, for the first time, we demonstrated that several primer pair (P3) located in non-FOXM1 binding region was used TF networks (FOXM1, MYC, APC/C/CDH1, E2F targets) and (Fig. 6D). ChIP-qPCR in C4-2 and 22Rv1 cells showed stronger oncogenic pathways (PLK1, mTORC1, Aurora B, Rho GTPases FOXM1 binding signals in the distal P1 region of the promoter signaling) are regulated by RRM2. All these TFs and signaling than in the proximal P2 region, supported by significant pathways have been reported to contribute to prostate cancer H3K4me3 binding in the same region (Fig. 6E). More significant progression. FOXM1 binding in 22Rv1 than in C4-2 could be due to higher Besides exploring RRM2 function, we uncovered the regula- expression of FOXM1 in 22Rv1 (Fig. 6H). As a positive control, we tory mechanisms of RRM2 overexpression in prostate cancer. assessed FOXM1 binding at the CENPF (identified FOXM1 target) The fact that more transcriptional activation of RRM2 was promoter in the same ChIP-qPCR. Strong FOXM1 binding at the observed in tumor tissue than in normal prostate tissue led CENPF promoter was detected in both cell lines (Supplementary us to develop a bioinformatic strategy to identify the RRM2- Fig. S7E and S7F). targeting TFs. Multiple ChIP-Seq in prostate cancer cells showed The activity of the RRM2 promoter reporter was reduced 40% in that the FOXM1 binding region of RRM2 promoter is highly siFOXM1-treated 22Rv1 cells (Fig. 6F), due to 80% reduction of transcriptionally activated. Thus, the cooperation of FOXM1 FOXM1 expression by siFOXM1 (Supplementary Fig. S7G). Con- and other TFs regulating RRM2 is a potential avenue for future sistently, siFOXM1 led to approximately 50% reduction of RRM2 study. FOXM1 was reported as a master regulator of the aggres- mRNA and protein expression (Fig. 6G and H). Besides siRNAs, a sive PCS1 subtype tumors (50). In our study, we identified small molecule inhibitor of FOXM1 (FDI-6) repressed multiple FOXM1 as the key regulator of RRM2, and inhibition of RRM2 FOXM1 targets, including RRM2, MYC, PSA, and CENPF (Fig. 6I). effectively repressed the FOXM1 pathway (Supplementary Altogether, FOXM1 can transcriptionally activate RRM2 expres- Fig. S4F and S5F), suggesting the feedback loop between sion by directly binding to the promoter. FOXM1 and RRM2. We believe that interruption of this regulatory feedback loop may specifically target aggressive subtype tumors. Discussion In summary, our study integrated the data from cell lines with The well-established function of RRM2 is to maintain the clinical cohorts to reveal that overexpression of RRM2 is associ- balance of dNTP pools for DNA synthesis and DNA repair. ated with poor outcomes and is potentially a driver of aggressive However, there has been no comprehensive mechanistic investi- prostate cancer. Use of an RRM2-targeted inhibitor does inhibit gation that defines the downstream biological consequences and RRM2 oncogenic activity in vivo. Furthermore, we defined the signaling pathways of RRM2 in cancer, specifically in prostate underlying molecular mechanisms and determined the transcrip- cancer. This study showed for the first time the oncogenic prop- tional activation of RRM2 by FOXM1. erties of RRM2 in prostate cancer. We unraveled and validated the significant prognostic value of RRM2 in 11 prostate cancer clinical Disclosure of Potential Conflicts of Interest cohorts. Integrative analysis of transcriptomic and phosphopro- Y.Z. Mazzu has filed a patent application relevant to the work that is the teomic changes regulated by RRM2 revealed oncogenic mechan- subject of this paper: U.S Provisional Patent Application No. 62/834,914; RRM2 fi isms of RRM2 in prostate cancer. Signature as a Prognostic Marker for Prostate Cancer Survival; led: April 16, Abnormal RRM2 degradation induced imbalance of the dNTP 2019; MSK Ref.: SK2019-043-01. P.W. Kantoff reports the following for the last 36-month period: investment interest in Context Therapeutics, DRGT, Placon, pool and genome instability (29). In our analysis, we found that Seer Biosciences, and Tarveda Therapeutics; company board member for Con- overexpressed RRM2 is highly correlated with deletion of CHEK2 text Therapeutics; consultant/scientific advisory board member for Bavarian or ATM, which may lead to higher FGA and Gleason score Nordic Immunotherapeutics, BIND Biosciences, DRGT, GE Healthcare, Janssen,

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Metamark, New England Research Institutes, OncoCellMDX, Progenity, Sanoﬁ, Administrative, technical, or material support (i.e., reporting or organizing Seer Biosciences, Tarveda Therapeutics, and Thermo Fisher; and data safety data, constructing databases): Y.Z. Mazzu, T.A. Gerke, X. Qiu, H. Zhao, monitoring board member for Genentech/Roche and Merck. No potential S.W. Fine conﬂicts of interest were disclosed by the other authors. Study supervision: Y.Z. Mazzu, G.-S.M. Lee, P.W. Kantoff

Authors' Contributions Acknowledgments Conception and design: Y.Z. Mazzu, P.W. Kantoff This research was funded in part through the NIH/NCI Cancer Center Development of methodology: Y.Z. Mazzu, G. Chakraborty, H. Zhao, Support Grant to Memorial Sloan Kettering Cancer Center (P30 CA008748), K. Komura, E. O'Connor, P.W. Kantoff and dNTP assays were funded in part through NIH/NIGMS R01 GM104198 to Acquisition of data (provided animals, acquired and managed patients, B. Kim and NIH/NIAID R01 AI136581 to B. Kim. provided facilities, etc.): Y.Z. Mazzu, Y. Yoshikawa, S.A. Coggins, M. Atiq, K. Komura, C. Bell, E. O'Connor, B. Kim Analysis and interpretation of data (e.g., statistical analysis, biostatistics, The costs of publication of this article were defrayed in part by the computational analysis): Y.Z. Mazzu, J. Armenia, G. Chakraborty, payment of page charges. This article must therefore be hereby marked advertisement S. Nandakumar, T.A. Gerke, M.M. Pomerantz, X. Qiu, K. Komura, G.-S.M. in accordance with 18 U.S.C. Section 1734 solely to indicate Lee, S.W. Fine, H.W. Long, M.L. Freedman, P.W. Kantoff this fact. Writing, review, and/or revision of the manuscript: Y.Z. Mazzu, J. Armenia, S. Nandakumar, T.A. Gerke, M.M. Pomerantz, M. Atiq, N. Khan, S.W. Fine, Received December 14, 2018; revised March 14, 2019; accepted April 8, 2019; M.L. Freedman, P.W. Kantoff published ﬁrst April 17, 2019.

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Targeting RRM2 Suppresses Poor-Prognosis Prostate Cancer

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www.aacrjournals.org Clin Cancer Res; 2019 OF13

A Novel Mechanism Driving Poor-Prognosis Prostate Cancer: Overexpression of the DNA Repair Gene, Ribonucleotide Reductase Small Subunit M2 (RRM2)

Ying Z. Mazzu, Joshua Armenia, Goutam Chakraborty, et al.

Clin Cancer Res Published OnlineFirst April 17, 2019.

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